File size: 6,591 Bytes
d8bc908 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 | from __future__ import annotations
from dataclasses import dataclass
from typing import Iterable
import torch
@dataclass
class TensorState:
name: str
shape: tuple[int, ...]
dtype: str
bytes: int
trainable: bool = False
@dataclass
class TernaryAudit:
logical_ternary_weights: int
ternary_packed_bytes: int
ternary_scale_bytes: int
ternary_scale_accum_bytes: int
ternary_accum_bytes: int
ternary_corr_accum_bytes: int
ternary_step_counter_bytes: int
trainable_float_params: list[TensorState]
frozen_float_params: list[TensorState]
float_buffers: list[TensorState]
@property
def ternary_training_bytes(self) -> int:
return (
self.ternary_packed_bytes
+ self.ternary_scale_bytes
+ self.ternary_scale_accum_bytes
+ self.ternary_accum_bytes
+ self.ternary_corr_accum_bytes
+ self.ternary_step_counter_bytes
)
@property
def trainable_float_bytes(self) -> int:
return sum(item.bytes for item in self.trainable_float_params)
@property
def frozen_float_bytes(self) -> int:
return sum(item.bytes for item in self.frozen_float_params)
@property
def float_buffer_bytes(self) -> int:
return sum(item.bytes for item in self.float_buffers)
def _tensor_bytes(t: torch.Tensor) -> int:
return t.numel() * t.element_size()
def _tensor_state(name: str, t: torch.Tensor, trainable: bool = False) -> TensorState:
return TensorState(
name=name,
shape=tuple(t.shape),
dtype=str(t.dtype).replace("torch.", ""),
bytes=_tensor_bytes(t),
trainable=trainable,
)
def _mb(n_bytes: int) -> float:
return n_bytes / (1024 * 1024)
def audit_model(model: torch.nn.Module) -> TernaryAudit:
logical_ternary_weights = 0
ternary_packed_bytes = 0
ternary_scale_bytes = 0
ternary_scale_accum_bytes = 0
ternary_accum_bytes = 0
ternary_corr_accum_bytes = 0
ternary_step_counter_bytes = 0
for module in model.modules():
if hasattr(module, "T_packed") and hasattr(module, "_T_shape"):
shape = tuple(int(x) for x in module._T_shape.tolist())
n_weights = 1
for dim in shape:
n_weights *= dim
logical_ternary_weights += n_weights
ternary_packed_bytes += _tensor_bytes(module.T_packed)
if hasattr(module, "E"):
ternary_scale_bytes += _tensor_bytes(module.E)
if hasattr(module, "E_accum"):
ternary_scale_accum_bytes += _tensor_bytes(module.E_accum)
if hasattr(module, "T_accum"):
ternary_accum_bytes += _tensor_bytes(module.T_accum)
if hasattr(module, "corr_accum"):
ternary_corr_accum_bytes += _tensor_bytes(module.corr_accum)
if hasattr(module, "step_counter"):
ternary_step_counter_bytes += _tensor_bytes(module.step_counter)
trainable_float_params: list[TensorState] = []
frozen_float_params: list[TensorState] = []
for name, param in model.named_parameters():
if not param.dtype.is_floating_point:
continue
state = _tensor_state(name, param, trainable=param.requires_grad)
if param.requires_grad:
trainable_float_params.append(state)
else:
frozen_float_params.append(state)
float_buffers = [
_tensor_state(name, buf)
for name, buf in model.named_buffers()
if buf.dtype.is_floating_point
]
return TernaryAudit(
logical_ternary_weights=logical_ternary_weights,
ternary_packed_bytes=ternary_packed_bytes,
ternary_scale_bytes=ternary_scale_bytes,
ternary_scale_accum_bytes=ternary_scale_accum_bytes,
ternary_accum_bytes=ternary_accum_bytes,
ternary_corr_accum_bytes=ternary_corr_accum_bytes,
ternary_step_counter_bytes=ternary_step_counter_bytes,
trainable_float_params=trainable_float_params,
frozen_float_params=frozen_float_params,
float_buffers=float_buffers,
)
def format_audit(audit: TernaryAudit, limit: int = 12) -> str:
lines = [
"Ternary state audit:",
f" logical ternary weights: {audit.logical_ternary_weights:,}",
(
" ternary training state: "
f"{_mb(audit.ternary_training_bytes):.2f} MB "
f"(T={_mb(audit.ternary_packed_bytes):.2f}, "
f"E={_mb(audit.ternary_scale_bytes):.2f}, "
f"E_accum={_mb(audit.ternary_scale_accum_bytes):.2f}, "
f"T_accum={_mb(audit.ternary_accum_bytes):.2f}, "
f"corr_accum={_mb(audit.ternary_corr_accum_bytes):.2f}, "
f"steps={_mb(audit.ternary_step_counter_bytes):.4f})"
),
(
" trainable float params: "
f"{len(audit.trainable_float_params)} tensors, "
f"{_mb(audit.trainable_float_bytes):.2f} MB"
),
(
" frozen float params: "
f"{len(audit.frozen_float_params)} tensors, "
f"{_mb(audit.frozen_float_bytes):.2f} MB"
),
(
" float buffers: "
f"{len(audit.float_buffers)} tensors, "
f"{_mb(audit.float_buffer_bytes):.2f} MB"
),
]
if audit.trainable_float_params:
lines.append(" largest trainable float params:")
for item in sorted(audit.trainable_float_params, key=lambda x: x.bytes, reverse=True)[:limit]:
lines.append(f" {item.name}: {item.shape} {item.dtype} {_mb(item.bytes):.2f} MB")
if audit.float_buffers:
lines.append(" largest float buffers:")
for item in sorted(audit.float_buffers, key=lambda x: x.bytes, reverse=True)[:limit]:
lines.append(f" {item.name}: {item.shape} {item.dtype} {_mb(item.bytes):.2f} MB")
return "\n".join(lines)
def freeze_float_parameters(
model: torch.nn.Module,
allow_prefixes: Iterable[str] = (),
) -> list[TensorState]:
allow = tuple(allow_prefixes)
frozen: list[TensorState] = []
for name, param in model.named_parameters():
if allow and name.startswith(allow):
continue
if param.dtype.is_floating_point and param.requires_grad:
frozen.append(_tensor_state(name, param, trainable=True))
param.requires_grad_(False)
return frozen
def trainable_parameters(model: torch.nn.Module) -> list[torch.nn.Parameter]:
return [p for p in model.parameters() if p.requires_grad]
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